BLOG

The Science of Prediction

 Using Big Data to see what’s in store for your company

 

Big data analytics can be found in most areas in business these days, and with good reason; the insights provided by data science have been game changing. Analysis into consumer behavior has revealed customers’ true spending habits, data scientists at manufacturing plants have discovered exact sources for inefficiency, and social media information has been studied to understand the interests of a certain type of client. But most content on the promise of big data analytics relies upon understanding the present - looking at what is actually happening in any given organization, to find clues to streamline and increase productivity.

But a more interesting focus is that of the future – using big data to provide insights of what is to come in a business, and direct the company to evolve towards these upcoming changes, to be ahead of the curve. As discussed earlier, big data analytics is nothing without the human element, in that the insights necessary to interpret patterns, make conclusions, and formulate decisions, all come from the human mind, the engine behind the big data juggernaut. This same mentality applies to prediction; when patterns emerge from analyzing a data set, it is up to the analyst to link this information with other sources to see patterns on the macro-level; that is to say, predicting the future. Take a look at some of the ways big data analytics is helping executives form logical predictions into some of our most important industries:

Healthcare

Healthcare and data were always a match made in cyberheaven. Almost every aspect of the healthcare industry, from patients’ medical records to public health research, is based on analyzing enormous amounts of data. As more and more healthy-living apps are entering the market like daily pedometers, diet planning and calorie counters, people may begin to actually share this information with their doctors. This allows for a more complete picture of a patient’s behaviors throughout the day, and will lead to a clearer picture of the health of that patient. On a bigger scale, big data has also begun to predict movements of epidemics on a nationwide level, allowing aid workers to distribute help based on need, and to construct a more effective approach towards combating illness.

Manufacturing

In the manufacturing industry, analyzing data based on inventory and production schedules was always a very important aspect of building an efficient business. But with the explosion of technology in data analysis, manufacturers can increase efficiency exponentially, thereby greatly increasing their margins. The focus today is on smart manufacturing; maximizing efficiency through the real-time collection and analysis of data in order to increase margins. Companies utilize sensors throughout their operation to gain insights into the manufacturing process. A great example is the Northern Indiana Public Service Company. NIPSCO operates thousands of miles of natural gas pipelines, most of which were built in the early 1900s. With specialized sensors put in place along the lines, NIPSCO continually monitors pressure, temperature and gas flow. Any abnormalities are reported directly to operators who can deal with the issue, by rerouting gas flows or shutting down parts of the distribution network. This avoids major disruption in gas service as well as increases pipeline safety.

Finance

Banks and financial institutions have always been hungry for information about their current and prospective clients. These institutions purchase data from retailers and service providers to find out enough information about a customer to create a 360-degree view of their financial character. This process is called customer segmentation. Data analysis focused on this information helps organizations understand and predict customer behavior, which in turn allows companies to cross-sell and up-sell their products. When data analytics’ insights reveal patterns in customer behavior, companies can approach these customers at the right time to upgrade or add new financial products, based on these patterns. This increases the value of each customer they have.

Big data analytics also plays a major role in risk management. Risk analytics, due to an ability to measure larger volumes of data faster, can deliver quicker reactions to new market developments and more precise fraud detection, thereby increasing efficiency.

 

The job of the visionary, and every company needs a visionary, is to look at the big picture. This sometimes means studying minutiae to find potentially advantageous or catastrophic implications for future business. Data analytics has allowed for a clearer picture of this minutiae, magnifying it for our eyes. Big data has brought the future into our present, so that we are able to discover insights previously unthinkable. But it’s important to remember that insights are only available to the insightful.